During the supervised training procedure, Adam [1]} is used as optimizer with initial learning rates according to the respective original implementations. The learning rate is reduced by a factor of 10 after 150 epochs of no decrease in the validation loss. All models are optimized using the source-to-distortion ratio (see Section REF ) between their outputs and the respective targets. We stopped all trainings after 1000 epochs (in one epoch the model iterates over the whole training set).
All models process audio sequences randomly extracted from each clip in the dataset; the length of the extracted sequences is equal to 2 seconds (2.3 seconds for CRAFx due to its architecture).
We used a batch size of 16 for all experiments.
